
Convergence analysis: Due to the limited number of resource blocks (RBs) in a wireless
network, only a subset of users can be selected to transmit their local FL model parameters to the BS at
each learning step. Moreover, since each user has unique training data samples, the BS prefers to include
all local user FL models to generate a converged global FL model. Hence, the FL performance and
convergence time will be significantly affected by the user selection scheme.
Most of the FL convergence proof is established on the assumption that the loss function in convex [1]}, [2]}.
However, the loss function of the popular neural network is non-convex. It is a challenge to investigate the convergence rate for FL with non-convex loss function.

Privacy and security:
In FL, the raw data set at each user can be protected since only the local FL model is transmitted to the BS.
However, it is also possible for Eavesdropper to reconstruct the raw data approximately, especially when the local and global model parameters are not well protected [3]}.
Besides, the local FL model may leak private information.
In FL, the security can be classified into two categories: global security and local security. Global privacy requires that the model updates generated at each round are private to all untrusted third
parties other than the central server, while local privacy further requires that the updates are also private to
the server.

Asynchronous communication: Fl involves the information exchange between wireless devices and the BS. Synchronous communication methods are simple, which introduce stragglers among all devices.
Asynchronous schemes are an attractive approach
to mitigate stragglers in heterogeneous environments.
server. While asynchronous parameter servers have been successful in distributed data centers, classical bounded-delay assumptions can be unrealistic in federated settings.

Non-iid device: Challenges arise when training federated models from data that is not identically distributed across devices,
both in terms of modeling the data, and in terms of analyzing the convergence
behavior of associated training procedures.
limited computation capacity at some wireless devices
causes delays.

Joint communication and computation design: To deploy FL in wireless networks, devices are required to transmit their multimedia data or local training results over unreliable wireless links. This exposes the performance of learning and inference to degradation caused by limited radio resources (e.g., power, time and bandwidth). This makes it important to jointly manage communication and computation resources for efficient and robust FL.

